Materialized views in data mining

Czejdo, Bogdan D.; Morzy, Mikołaj; Wojciechowski, Marek; Zakrzewicz, Maciej

doi:10.1109/dexa.2002.1046000

Cited by 6 publications

(9 citation statements)

References 18 publications

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“…Han on utilizing OLAP cube to create an OLAP-like mining environment [10][15], iceberg cube [8], OLAM cube [16], and materialized data mining views [7], etc.…”

Section: Discussionmentioning

confidence: 99%

“…In summary, all work on mining generalized association rules required the taxonomies to be static and ignored the fact that taxonomies of items may change over time or be adjusted by the analysts [11] [7]. This paper is the first, to our knowledge, to propose methods for solving this problem.…”

Section: Related Workmentioning

confidence: 99%

“…However, the taxonomies of items may change as time passes [7], and thus some items will be reclassified from one classification tree to another. For example, IBM TP could be reclassified from PC (Personal Computer) to MC (Mobile Computer) group.…”

Section: Desktop Pc  Epson Eplmentioning

confidence: 99%

“…After an initial discovery of all the generalized association rules in DB, let L be the set of all generalized frequent itemsets with respect to ms. As time passes, some update activities may occur to the taxonomies due to various reasons [11] [7]. Let T  denote the updated taxonomies.…”

Section: Problem Descriptionmentioning

confidence: 99%

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Updating generalized association rules with evolving taxonomies

2007

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Mining generalized association rules among items in the presence of taxonomies has been recognized as an important model for data mining. Earlier work on mining generalized association rules, however, required the taxonomies to be static, ignoring the fact that the taxonomies of items cannot necessarily be kept unchanged. For instance, some items may be reclassified from one hierarchy tree to another for more suitable classification, abandoned from the taxonomies if they will no longer be produced, or added into the taxonomies as new items. Additionally, the analysts might have to dynamically adjust the taxonomies from different viewpoints so as to discover more informative rules. Under these circumstances, effectively updating the discovered generalized association rules is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called Diff_ET and Diff_ET2, to update the discovered frequent itemsets. Empirical evaluation shows that the proposed algorithms are very effective and have good linear scale-up characteristics.2

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“…Han on utilizing OLAP cube to create an OLAP-like mining environment [10][15], iceberg cube [8], OLAM cube [16], and materialized data mining views [7], etc.…”

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Desktop Pc  Epson Eplmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

See 2 more Smart Citations

Updating generalized association rules with evolving taxonomies

2007

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“…In this paper [8]as the pattern discovery takes place mainly in the data warehouse environment, such long processing times are unacceptable from the point of view of interactive data mining. On the other hand, the results of consecutive data mining queries are usually very similar.…”

Section: Previous Workmentioning

confidence: 99%

A comparative study analysis of materialized view for selection cost

Nalini¹

2012

IJCSES

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An overview of data mining

Wang

Qiao

2010

2010 8th World Congress on Intelligent Control and Automation

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With the rapid development of information technology, the scale of a database increasingly grows. The problem, how to translate the large number data in a database into useful information and knowledge, is concerned by the academies and industries.At this point, data mining emerges and receives great concerns from the information industry and the whole society. In this paper, we firstly describe the development history of the data mining technology. Then their main methods and the application fields are summarized, and the ways to realize data mining is given. Finally, their future trends are discussed.

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Materialized views in data mining

Abstract: Abstract

Cited by 6 publications

References 18 publications

Updating generalized association rules with evolving taxonomies

Updating generalized association rules with evolving taxonomies

A comparative study analysis of materialized view for selection cost

An overview of data mining

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